Comparison of process-based and lumped parameter models for projecting future changes in fluvial sediment supply to the coast

Fluvial sediment supply (FSS) is one of the primary sources of sediment received by coasts. Any significant change in sediment supply to the coast will disturb its equilibrium state. Therefore, a robust assessment of future changes in FSS is required to understand the coastal system’s status under plausible climatic variations and human activities. Here, we investigate two modelling approaches to estimate the FSS at two spatially heterogeneous river basins: the Irrawaddy River Basin (IRB), Myanmar and the Kalu River Basin (KRB), Sri Lanka. We compare the FSS obtained from a process-based model (i.e., Soil Water Assessment Tool: SWAT) and an empirical model (i.e., the BQART model) for mid- (2046–2065) and end-century (2081–2100) periods under climate change and human activities (viz, planned reservoirs considered here). Our results show that SWAT simulations project a higher sediment load than BQART in the IRB and vice versa in KRB (for both future periods considered). SWAT projects higher percentage changes for both future periods (relative to baseline) compared to BQART projections in both basins with climate change alone (i.e., no reservoirs) and vice versa when planned reservoirs are considered. The difference between the two model projections (from SWAT and BQART) is higher in KRB, and it may imply that empirical BQART model projections are more in line with semi-distributed SWAT projections at the larger Irrawaddy River Basin than in the smaller Kalu River Basin

Hydrological Impacts Of Climate Change – Challenges, Uncertainty And Limitations

How climate change impacts water resources in the future is an important question that all hydrologists want to have an answer. Climate prediction scenarios are available from many Global Circulation Models for the 21st century. These prediction datasets are typically used as input to a hydrological model for simulating impacts on hydrology, particularly river runoff, evaporation, and storage changes. Because hydrological models are usually run on a much smaller resolutions than climate models, the climate prediction datasets are usually downscaled to represent local climate for using in a hydrological model. The uncertainty in the GCMs, downscaling and hydrological models makes the process complicated and heavily restricts our ability to make predictions of hydrological impacts. This becomes more challenging in a mountainous catchment where the availability of hydro-climatic data are limited. We illustrated some of these issues and their impacts on hydrological simulations using two catchments from the Himalayan region: the Koshi River (~58,000 km2), Nepal, and the source region of the Yellow River (~120,000 km2), China. Climate predictions used are from a number of GCMs participated in the Coupled Model Intercomparison Project (CMIP3). In both examples we used process-based distributed hydrological models: the Soil and Water Assessement Tool (SWAT) for the Koshi and WaSiM for the Yellow River

The 2022 drought needs to be a turning point for European drought risk management

The 2022 European drought has underscored critical deficiencies in European water management. This paper explores these shortcomings and suggests a way forward for European drought risk management. Data for this study was gathered through a continent-wide survey of water managers involved in this event. The survey collected 481 responses from 30 European countries and is comprised of 19 questions concerning sectorial impact in the 55 regions of the responders and drought risk management practices of their organizations. Information from the survey is enriched with climate-related information to offer a comprehensive overview of drought risk management in Europe. Our research focuses on four key aspects: the increasing risk of drought, its spatial and temporal impacts, current drought risk management approaches, and the evolution of drought risk management across the continent. Our findings reveal a consensus on the growing risk of drought, which is confounded by the rising frequency and intensity of droughts. While the 2022 event affected most of the continent, our findings show significant regional disparities in drought risk management capacity among the various countries. Our analysis indicates that current drought risk management measures often rely on short-term operational concerns, particularly in agriculture-dominated economies, leading to potentially maladaptive practices. An overall positive trend in drought risk management, with organizations showing increased awareness and preparedness, indicates how this crisis can be the ideal moment to mainstream European-wide drought risk management. Consequently, we advocate for a European Drought Directive, to harmonize and enforce drought risk management policies across the continent. This directive should promote a systemic, integrated, and long-term risk management perspective. The directive should also set clear guidelines for drought risk management at the national level and for cross-boundary drought collaboration. This study and its companion paper "The 2022 Drought Shows the Importance of Preparedness in European Drought Risk Management " are the result of a study carried out by the Drought in the Anthropocene (DitA) network

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

Improved first-order second moment method for uncertainty estimation in flood forecasting

The first-order second moment (FOSM) method is widely used inuncertainty analysis. This method uses a linearization of the function that relates theinput variables and parameters to the output variables. This simplification occasionallyleads to problems when the mean value of the input variable is close to a local orglobal maximum or minimum value of the function. In this case, the FOSM computesartificially a zero uncertainty because the first derivative of the function is equal tozero. An improvement to the FOSM is proposed, whereby a parabolic reconstructionis used instead of a linear one. The improved FOSM method is applied to a floodforecasting model on the Loire River (France). Verification of the method using theMonte Carlo technique shows that the improved FOSM allows the accuracy of theuncertainty assessment to be increased substantially, without adding a significantburden in computation. The sensitivity of the results to the size of the perturbation isalso analysed

Hydrological model calibration with streamflow and remote sensing based evapotranspiration data in a data poor basin

Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE > 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set

Effects of different precipitation inputs on streamflow simulation in the Irrawaddy River Basin, Myanmar

Study region: The Irrawaddy River Basin, Myanmar. Study focus: Precipitation is the most important input variable to numerically simulate the hydrological responses of a river basin. Nowadays, a number of precipitation data products with different spatial and temporal resolutions are available. However, the accuracy of these products may vary greatly and the variations may themselves differ in different river basins. Such differences have direct implications on the use of these datasets in hydrological modelling. Here, using a hydrological model, we investigated the effects of four precipitation datasets (in-situ gauge precipitation with and without interpolation, PERSIANN-CDR, and CHIRPS) on streamflow simulations in the Irrawaddy Basin in Myanmar. New hydrological insights for the study region: We identified considerable differences in streamflow simulation with the use of different precipitation inputs. The four datasets showed varied annual and seasonal precipitation values over the basin. Although the gauge density within the study area is very low, streamflow simulations forced with interpolated gauge data outperformed the models forced with other datasets. However, simulations forced with CHIRPS and PERSIANN-CDR also showed good results in most cases in terms of Nash Efficiency and R2, but mostly with high biases. In calibration, the four precipitation inputs resulted in varied best-fitted parameter values and ranges. All the above observations indicate that the selection of suitable precipitation input(s) is necessary for an accurate investigation of the hydrological responses of any given basin. Keywords: Irrawaddy River Basin, Spatial and temporal variabilities of precipitation, Streamflow simulatio